The results presented highlight some ML success against baseline forecasts and also a number of unique avenues that could be explored moving forward to enhance and improve both ML-based guidance and the SPC human-based forecasts, as well as increase interpretability of the ML `black box'. While the feature assembly experiments (e.g., p2, tl10) did not yield forecasts that surpassed the skill of the traditional CSU-MLP system, the simplification of features could be exploited to include other ensemble diagnostic or summary metrics (e.g., mean, high or low member values) that characterize ensemble spread into the medium range. The meteorological predictors could also be varied in any of the ML configurations to explore which predictors add the most value, or objective methods (e.g., permutation importance) could be used to reduce feature redundancy and select a more optimal subset of features. It will also be vitally important that alternative interpretability metrics (e.g., tree interpreter [1]}, Shapely additive values [2]}, accumulated local effect [3]}) are employed to interrogate how the RFs make predictions; this exploration is underway and will be the focus of a follow-on manuscript. Additionally, the added benefit of the ML system against the underlying GEFS model could be quantified more explicitly. Traditionally, ML-based forecasts have been measured against the very model that generates the ML predictors, with demonstrated success improving upon the raw dynamical models [4]}. In this instance, with notable 2-m dry and low-instability biases in the GEFSv12 systemInternal SPC surveys have suggested these biases exist and are reducing forecaster confidence in deterministic Global Forecast System and GEFSv12 forecasts [5]}, it would be informative to quantify the value added by the ML system to correct for these biases when making severe weather predictions.
